Channel detection system and method

ABSTRACT

Systems, methods, and computer-readable media are provided for detecting a channel behind casing and generating an image that represents the channel. An example method can include receiving data samples associated with at least one casing, each data sample representing channel information behind a representative casing, training a machine learning model using the data samples to generate a mapping between waveform information in each of the data samples and the channel information behind the representative casing, receiving acoustic data from a tool, the acoustic data representing a particular casing, and using the machine learning model to analyze the acoustic data from the tool and determine one of a presence and an absence of a channel behind the particular casing at a plurality of depths.

TECHNICAL FIELD

The present technology pertains to detecting channels behind casing, andmore specifically generating images that represent the channels.

BACKGROUND

Interzonal channeling can cause problems such as water production anddepletion of gas drive mechanism, among other problems. It is importantto determine the presence of channels and their locations to alleviatetheir impact on hydrocarbon production. Factors such as mud in awellbore may affect existing solutions. The conventional solutions havenot been accurate or robust enough to rely upon resulting in problemsthat cannot be adequately addressed.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and otheradvantages and features of the disclosure can be obtained, a moreparticular description of the principles briefly described above will berendered by reference to specific embodiments thereof which areillustrated in the appended drawings. Understanding that these drawingsdepict only exemplary embodiments of the disclosure and are nottherefore to be considered to be limiting of its scope, the principlesherein are described and explained with additional specificity anddetail through the use of the accompanying drawings in which:

FIG. 1A is a schematic diagram of an example logging while drilling(LWD) wellbore operating environment, in accordance with some examples;

FIG. 1B is a schematic diagram of an example downhole environment with aconveyance, in accordance with some examples;

FIG. 2 is a block diagram of an example channel detection system whichmay be implemented to detect channels behind casing and generate images,in accordance with some examples;

FIG. 3 is a block diagram of an example acoustic tool associated withthe system, in accordance with some examples;

FIG. 4 is a graph of example waveform derived attribute data used totrain a machine learning model, in accordance with some examples;

FIG. 5 is another graph of example waveform derived attribute data usedto train the machine learning model, in accordance with some examples;

FIG. 6 is an image representing an actual channel size and location, inaccordance with some examples;

FIG. 7 is another image representing a predicted channel size andlocation, in accordance with some examples;

FIG. 8 is a flowchart of an example method for detecting a channelbehind casing and generating an image that represents the channel, inaccordance with some examples;

FIG. 9 is another flowchart of an example method for determining anintegral amplitude attribute used to detect the channel, in accordancewith some examples;

FIG. 10 is a flowchart of an example method for detecting a channelbehind casing and generating an image that represents the channel, inaccordance with some examples;

FIG. 11 is a schematic diagram of an example computing devicearchitecture, in accordance with some examples.

DETAILED DESCRIPTION

Various embodiments of the disclosure are discussed in detail below.While specific implementations are discussed, it should be understoodthat this is done for illustration purposes only. A person skilled inthe relevant art will recognize that other components and configurationsmay be used without parting from the spirit and scope of the disclosure.

Additional features and advantages of the disclosure will be set forthin the description which follows, and in part will be obvious from thedescription, or can be learned by practice of the herein disclosedprinciples. The features and advantages of the disclosure can berealized and obtained by means of the instruments and combinationsparticularly pointed out in the appended claims. These and otherfeatures of the disclosure will become more fully apparent from thefollowing description and appended claims, or can be learned by thepractice of the principles set forth herein.

It will be appreciated that for simplicity and clarity of illustration,where appropriate, reference numerals have been repeated among thedifferent figures to indicate corresponding or analogous elements. Inaddition, numerous specific details are set forth in order to provide athorough understanding of the embodiments described herein. However, itwill be understood by those of ordinary skill in the art that theembodiments described herein can be practiced without these specificdetails. In other instances, methods, procedures and components have notbeen described in detail so as not to obscure the related relevantfeature being described. The drawings are not necessarily to scale andthe proportions of certain parts may be exaggerated to better illustratedetails and features. The description is not to be considered aslimiting the scope of the embodiments described herein.

Disclosed are systems, methods, and computer-readable storage media fordetecting channels behind casing, and generating images that representthe channels. The channels may be detected using a monopole acousticmeasurement tool having one or more azimuthally arranged receiver ringsalong the circumference of the tool. The receivers may collect waveformsand determine integral amplitudes based on the waveform data. The datamay be used to determine azimuthal location and a size of a channel in acasing by using a machine learning model at a number of depths ofinterest. A two dimensional image or map can be generated thatrepresents the location and size of the channel in the casing at thenumber of depths of interest.

According to at least one aspect, an example method for detectingchannels behind casing, and generating images that represent thechannels is provided. The method can include receiving, by at least oneprocessor, data samples associated with at least one casing, each datasample representing channel information behind a representative casing,training, by the at least one processor, a machine learning model usingthe data samples to generate a mapping between waveform information ineach of the data samples and the channel information behind therepresentative casing, receiving, by the at least one processor,acoustic data from a tool, the acoustic data representing a particularcasing, and using, by the at least one processor, the machine learningmodel to analyze the acoustic data from the tool and determine one of apresence and an absence of a channel behind the particular casing at aplurality of depths.

According to at least one aspect, an example system for detectingchannels behind casing, and generating images that represent thechannels is provided. The system can include one or more processors andat least one computer-readable storage medium having stored thereininstructions which, when executed by the one or more processors, causethe system to receive data samples associated with a at least onecasing, each data sample representing channel information behind arepresentative casing, train a machine learning model using the datasamples to generate a mapping between waveform information in each ofthe data samples and the channel information behind the representativecasing, receive acoustic data from an acoustic tool, the acoustic datarepresenting a particular casing, and use the machine learning model toanalyze the acoustic data from the tool and determine one of a presenceand an absence of a channel behind the particular casing at a pluralityof depths.

According to at least one aspect, an example non-transitorycomputer-readable storage medium for detecting channels behind casing,and generating images that represent the channels is provided. Thenon-transitory computer-readable storage medium can include instructionswhich, when executed by one or more processors, cause the one or moreprocessors to receive data samples associated with a at least onecasing, each data sample representing channel information behind arepresentative casing, train a machine learning model using the datasamples to generate a mapping between waveform information in each ofthe data samples and the channel information behind the representativecasing, receive acoustic data from a tool, the acoustic datarepresenting a particular casing, and use the machine learning model toanalyze the acoustic data from the tool and determine one of a presenceand an absence of a channel behind the particular casing at a pluralityof depths.

In some aspects, the systems, methods, and non-transitorycomputer-readable storage media described above can include generatingan azimuthal cement bond depth channel image that represents thepresence and the absence of the channel behind the particular casing atthe plurality of depths. The channel image may be a binarytwo-dimensional image. In addition, the binary two-dimensional image canrepresent a size and an azimuthal location of the channel behind theparticular casing at the plurality of depths. The machine learning modelcan be based on random forest, among other algorithms.

Additionally, the tool can be at least one array of receiversazimuthally arranged along a circumference of the tool. The array ofreceivers can determine an integral amplitude for waveform data obtainedby the at least one array of receivers. In one example, the tool mayhave eight arrays of receivers that are azimuthally arranged. Each arraymay have thirteen receivers. At an offset of three feet from themonopole source, there may be a ring of eight receivers. Similarly, atan offset of five feet from monopole source there may be a ring ofreceivers.

As follows, the disclosure will provide a more detailed description ofthe systems, methods, computer-readable media and techniques herein fordetecting channels behind casing, and generating images that representthe channels. The disclosure will begin with a description of examplesystems and environments, as shown in FIGS. 1A-7. A description ofexample methods and technologies for detecting channels behind casing,and generating images that represent the channels, as shown in FIGS. 8,9, and 10 will then follow. The disclosure concludes with a descriptionof an example computing system architecture, as shown in FIG. 11, whichcan be implemented for performing computing operations and functionsdisclosed herein. These variations shall be described herein as thevarious embodiments are set forth.

The disclosure now turns to FIG. 1A, which illustrates a schematic viewof a logging while drilling (LWD) wellbore operating environment 100 inaccordance with some examples of the present disclosure. As depicted inFIG. 1A, a drilling platform 102 can be equipped with a derrick 104 thatsupports a hoist 106 for raising and lowering a drill string 108. Thehoist 106 suspends a top drive 110 suitable for rotating and loweringthe drill string 108 through a well head 112. A drill bit 114 can beconnected to the lower end of the drill string 108. As the drill bit 114rotates, the drill bit 114 creates a wellbore 116 that passes throughvarious formations 118. A pump 120 circulates drilling fluid through asupply pipe 122 to top drive 110, down through the interior of drillstring 108 and orifices in drill bit 114, back to the surface via theannulus around drill string 108, and into a retention pit 124. Thedrilling fluid transports cuttings from the wellbore 116 into theretention pit 124 and aids in maintaining the integrity of the wellbore116. Various materials can be used for drilling fluid, includingoil-based fluids and water-based fluids.

Logging tools 126 can be integrated into the bottom-hole assembly 125near the drill bit 114. As the drill bit 114 extends the wellbore 116through the formations 118, logging tools 126 collect measurementsrelating to various formation properties as well as the orientation ofthe tool and various other drilling conditions. The bottom-hole assembly125 may also include a telemetry sub 128 to transfer measurement data toa surface receiver 132 and to receive commands from the surface. In atleast some cases, the telemetry sub 128 communicates with a surfacereceiver 132 using mud pulse telemetry. In some instances, the telemetrysub 128 does not communicate with the surface, but rather stores loggingdata for later retrieval at the surface when the logging assembly isrecovered.

Each of the logging tools 126 may include one or more tool componentsspaced apart from each other and communicatively coupled with one ormore wires and/or other media. The logging tools 126 may also includeone or more computing devices 134 communicatively coupled with one ormore of the one or more tool components by one or more wires and/orother media. The one or more computing devices 134 may be configured tocontrol or monitor a performance of the tool, process logging data,and/or carry out one or more aspects of the methods and processes of thepresent disclosure.

In at least some instances, one or more of the logging tools 126 maycommunicate with a surface receiver 132 by a wire, such as wireddrillpipe. In other cases, the one or more of the logging tools 126 maycommunicate with a surface receiver 132 by wireless signal transmission.In at least some cases, one or more of the logging tools 126 may receiveelectrical power from a wire that extends to the surface, includingwires extending through a wired drillpipe.

Referring to FIG. 1B, an example system 140 for downhole line detectionin a downhole environment with a conveyance can employ a tool having atool body 146 in order to carry out logging and/or other operations. Forexample, instead of using the drill string 108 of FIG. 1A to lower toolbody 146, which may contain sensors or other instrumentation fordetecting and logging nearby characteristics and conditions of thewellbore 116 and surrounding formation, a wireline conveyance 144 can beused. The tool body 146 can include a resistivity logging tool. The toolbody 146 can be lowered into the wellbore 116 by wireline conveyance144. The wireline conveyance 144 can be anchored in the drill rig 145 ora portable means such as a truck. The wireline conveyance 144 caninclude one or more wires, slicklines, cables, and/or the like, as wellas tubular conveyances such as coiled tubing, joint tubing, or othertubulars.

The illustrated wireline conveyance 144 provides support for the tool,as well as enabling communication between tool processors 148A-N on thesurface and providing a power supply. In some examples, the wirelineconveyance 144 can include electrical and/or fiber optic cabling forcarrying out communications. The wireline conveyance 144 is sufficientlystrong and flexible to tether the tool body 146 through the wellbore116, while also permitting communication through the wireline conveyance144 to one or more processors 148A-N, which can include local and/orremote processors. Moreover, power can be supplied via the wirelineconveyance 144 to meet power requirements of the tool. For slickline orcoiled tubing configurations, power can be supplied downhole with abattery or via a downhole generator.

Disclosed herein are systems and methods for detecting channels behindcasing and using a machine learning based approach and generating imagesthat represent the channels behind the casing. An image may be anazimuthal cement bond depth image that may be generated using one ormore receiver rings azimuthally arranged along a circumference of amonopole acoustic measurement tool. There may any number of receiverrings located at different offsets to generate the image. The method fordetecting channels may use a portion of a waveform as detected by theacoustic measurement tool. Conventionally, an E1 peak in the waveformdata may have been used. However, the systems and methods discussedherein provide a more robust image that is less affected by noise. Inaddition, the systems and methods discussed herein utilize machinelearning to avoid effects of factors affecting data that cannot becorrected effectively (e.g., mud effects and receiver azimuthalresponses at higher frequencies used for cement evaluation).

The system may utilize eight azimuthal receivers including one fixedsource-receiver offset or multiple receivers' offset waveform data at anumber of depths and may apply preprocessing steps such as resamplingand de-trending on the data. The system may then determine an amplitudebased attribute (e.g., integral amplitude) for each of the eightreceivers. Each of the receivers may be an array of receivers. If thesystem uses multiple source-receiver offsets, the ring generatedattributes may be stacked or combined together to provide more robustcalculations. The system may analyze the attributes to determine anazimuthal location and an angular extent of a channel at each depthusing a machine learning regression model. Multiple machine learningmodels may be generated for differing casings and mud specifications anda particular machine learning model may be selected for a particularcasing and mud being analyzed.

In one example, the machine learning models may be trained with datathat may be collected from controlled laboratory environments and/orsimulation data. As a result, each machine learning model may be used todetermine channel size and location using a particular tool. The systemmay determine the channel size and azimuthal location and generate anazimuthal cement bond image that may indicate a presence or the absenceof a channel. The image may indicate the presence or absence in theimage using a binary mapping. As a result, the image may provide atwo-dimensional attribute map that is based on amplitude attributes fromthe data that may be interpolated and smoothed. The system may be usedto detect narrow channels because the machine learning models mayconvert lower magnitude casing wave amplitude attributes to acorresponding channel specification (e.g., a channel size and location).

The machine learning regression based models may be trained with sonictool measurements to predict size and location of a channel behindcasing. The data may be numerically simulated data and used to train themachine learning algorithm with tool measured waveform data. As aresult, the system may be used to provide proof and evidence of thepresence of channels with monopole acoustic tools.

Interzonal channeling may cause problems such as water production anddepletion of the gas drive mechanism, among others. As a result,detecting the presence of channels and determining their azimuthallocation may be important to alleviate impacts on hydrocarbonproduction.

Conventional solutions may provide high resolution evaluations of thestate of cement behind casing using ultrasonic data. A radial bondlogging tool may provide cement bond using E1 peak data from sonic datacollected using receivers. However, the images are not reliable or asaccurate as the system discussed herein. The system is able to utilize anumber of azimuthally distributed receivers to generate azimuthal imagesfrom sonic data using monopole transmitter tools. E1 peaks may beaffected by receiver responses that vary azimuthally at relativelyhigher frequencies. In addition, mud may affect amplitudes. Thesefactors may affect the conventional images such that they are notreliable.

The system discussed herein may detect channels with more confidencethan conventional solutions that may be prone to effects of noise. As aresult, the system may be used to provide an azimuthal ultrasonic cementevaluation with a lower frequency/deeper reading sonic frequency result.

In one example, one or more source-receiver offsets may be used togenerate an azimuthal cement bond image. For a chosen source-receiveroffset, at each depth, the system may obtain eight azimuthallydistributed fixed source-receiver offset waveform data. The waveformsmay be resampled. As an example, they may be resampled at fivemicroseconds instead of ten microseconds and background trends may beremoved.

A time window may be defined by identifying a beginning of a casing wavein the waveform data. The time window may be fixed as long as the casingpipe specifications do not change. Once the time window is defined, anamplitude based attribute (e.g., integral amplitude) may be determinedfor each of the eight waveforms. Determination of the integral amplitudefor a waveform data may be accomplished by determining a peaks basedenvelope of the rectified waveform and using the envelope to modulate asinusoid signal. A 30 kHz signal may be used as an example. A firstperiod of the modulated signal may be integrated to obtain an integralamplitude attribute. Eight values may then be used as features for amachine learning regression model to predict channel size and azimuthallocation.

The process may be repeated for a number of depths to produce atwo-dimensional channel distribution map image. More than onesource-receiver offset may be used such as one receiver three feet awayand another receiver five feet away. Individually generated amplitudeattributes may be combined and stacked together to reduce noise. Thechannel map may be generated from the multiple receiver data using thetrained machine learning model.

The machine learning model may be trained to predict channel size andlocation provided the eight integral amplitude values from thereceivers. The machine learning model may be based on random forestand/or another machine learning algorithm such as support vector machine(SVM) and neural networks. As an example, 10,000 data samples may beused to train the random forest machine learning model. As an example, arandom forest may be an ensemble learning method for classification andregression. It may operate by constructing a multitude of decision treeswhile training on provided data. The random forest may output a meanprediction of the individual trees. Random forests may be less prone tobias and overfitting as opposed to other machine learning algorithms.The random forest algorithm may be trained using data (e.g., eightintegral amplitude values used as features and corresponding channelsizes and locations as realizations of the dependent variables) andgenerated under controlled conditions for various casing sizes and mudconditions. The training may be used to produce a direct mapping betweenmeasurement distribution and channel specification. As a result, themachine learning model may be used to collect data and predict a channelin real-time. In one example, the random forest is trained with datacollected by the sonic tool under controlled conditions and thenutilized to predict the channel with actual data in real-time.

FIG. 2 illustrates a channel detection system 200. The channel detectionsystem 200 can be implemented for detecting channels behind casing, andgenerating images that represent the channels as described herein. Inthis example, the channel detection system 200 can include computecomponents 202, waveform collection engine 204, imaging engine 206, astorage 208, and a tool or device 212. In some implementations, thechannel detection system 200 can also include a display device 210 fordisplaying data and graphical elements such as images, videos, text,simulations, and any other media or data content.

The tool 212 may be a monopole acoustic measurement tool or device thatincludes one or more receiver rings azimuthally arranged along thecircumference of the tool. There may be any number of rings (e.g.,eight) located at different offsets to receive and collect waveform datathat may be used to generate an image.

The channel detection system 200 can be part of, or implemented by, oneor more computing devices, such as one or more servers, one or morepersonal computers, one or more processors, one or more mobile devices(for example, a smartphone, a camera, a laptop computer, a tabletcomputer, a smart device, etc.), and/or any other suitable electronicdevice. In some cases, the one or more computing devices that include orimplement the channel detection system 200 can include one or morehardware components such as, for example, one or more wirelesstransceivers, one or more input devices, one or more output devices (forexample, display device 210), one or more sensors (for example, an imagesensor, a temperature sensor, a pressure sensor, an altitude sensor, aproximity sensor, an inertial measurement unit, etc.), one or morestorage devices (for example, storage system 208), one or moreprocessing devices (for example, compute components 202), etc.

As previously mentioned, the channel detection system 200 can includecompute components 202. The compute components can be used to implementthe waveform collection engine 204, the imaging engine 206, and/or anyother computing component. The compute components 202 can also be usedto control, communicate with, and/or interact with the storage 208and/or the display device 210. The compute components 202 can includeelectronic circuits and/or other electronic hardware, such as, forexample and without limitation, one or more programmable electroniccircuits. For example, the compute components 202 can include one ormore microprocessors, one or more graphics processing units (GPUs), oneor more digital signal processors (DSPs), one or more central processingunits (CPUs), one or more image signal processors (ISPs), and/or anyother suitable electronic circuits and/or hardware. Moreover, thecompute components 202 can include and/or can be implemented usingcomputer software, firmware, or any combination thereof, to perform thevarious operations described herein.

The waveform engine 204 can be used to receive data samples associatedwith training a machine learning algorithm such as random forest. As anexample, the waveform engine 204 may receive approximately 10,000 datasamples to train the random forest. The data samples may be collected bythe tool 212 and/or may be synthetically generated or simulated. Thedata samples may represent waveforms or casing waves that may begenerated under controlled conditions for various casing sizes and mudconditions. As a result, the machine learning algorithm may be trainedwith the data samples to produce a direct mapping between the datasamples and channel specifications such as channel size and location.

The waveform engine 204 can be used to determine integral amplitudes forthe azimuthal receivers associated with the tool 212. The trained modelmay then be used to predict channels using the integral amplitudes fromsonic waveform data at depths of interest.

Based on the predicted channel information, the imaging engine 206 maygenerate a two dimensional image or map that indicates the presenceand/or absence of channels. As an example, the two dimensional image mayindicate a predicted channel size and location. The image may indicatewhere the channel is located in the azimuth in degrees and depth, amongother information.

The storage 208 can be any storage device(s) for storing data. In someexamples, the storage 208 can include a buffer or cache for storing datafor processing by the compute components 202. Moreover, the storage 208can store data from any of the components of the memory tool activationand control system 200. For example, the storage 208 can store inputdata used by the channel detection system 200, outputs or resultsgenerated by the channel detection system 200 (for example, data and/orcalculations from the waveform collection engine 204, the imaging engine206, etc.), user preferences, parameters and configurations, data logs,documents, software, media items, GUI content, and/or any other data andcontent.

While the channel detection system 200 is shown in FIG. 2 to includecertain components, one of ordinary skill in the art will appreciatethat the channel detection system 200 can include more or fewercomponents than those shown in FIG. 2. For example, the channeldetection system 200 can also include one or more memory components (forexample, one or more RAMs, ROMs, caches, buffers, and/or the like), oneor more input components, one or more output components, one or moreprocessing devices, and/or one or more hardware components that are notshown in FIG. 2.

FIG. 3 shows an example acoustic tool 300 associated with the systemaccording to an example. A monopole source is shown at 302. As shown inFIG. 3, a two-dimensional view of the tool shows three visible arrays310 of the tool 300. As an example, eight such azimuthally arrangedarrays 310 having 45 degrees of separation may be present on the tool300. As noted above, each array 310 may have thirteen receivers 312,although only eight receivers are shown in each array 310 in FIG. 3. Theother five arrays of receivers are not visible in this two-dimensionalview of the tool 300. The design of the arrays is not limited to thisarrangement and may differ. As an example, at a particular offset, theremay be a ring of receivers 304. Each ring of receivers may include eightreceivers, or another number of receivers. A ring of receivers 306 maybe at one offset, e.g., three feet from the source. A ring of receivers308 may be at a second offset, e.g., five feet from the source. Theremay be a spacing between each ring of receivers, e.g., 0.5 feet.

FIG. 4 is a graph 400 of example waveform data used to train a machinelearning model according to an example. FIG. 4 shows a graph of noisefree data from a numerical simulation including data collected from eachreceiver and an associated amplitude. The plot shown includes theintegral amplitudes for eight azimuthal receivers in the presence of aninety degree channel in the cement and it may be generated using finitedifference simulations.

The data shown represents a plot of integral amplitudes with a ninetydegree channel in cement located at one hundred and twenty degrees withrespect to the reference axis. The waveform data may be determined usingfinite difference code. As a result, the integral amplitudes may bedetermined from controlled experiment data similarly for training andintegral amplitudes collected from data may be used with the trainedmachine learning model to predict channels.

FIG. 5 is a graph 500 of example waveform data used to train the machinelearning model according to an example. FIG. 5 shows a graph of noisecorrupted data including data collected from each receiver and anassociated amplitude. The plot shown in FIG. 5 indicates the integralamplitudes that are multiplied with an artificial azimuthal response andadding 10% random noise. In other words, the data shown represents aplot of integral amplitudes with a ninety degree channel in cementlocated at one hundred and twenty degrees with respect to the referenceaxis. The values may be multiplied with an artificial azimuthal receiverresponse function and as shown random noise may be added. As a result,the waveform data may be determined using finite difference code.

FIG. 6 is an image 600 representing an actual channel size and locationaccording to an example. FIG. 6 shows an actual channel image for asynthetic test. As shown in FIG. 6, the first section 602 represents achannel and a second section 604 represents an absence of the channel.FIG. 6 indicates a size and location of the channel with respect to adepth level index and an azimuth in degrees.

FIG. 7 is an image 700 representing a predicted channel size andlocation as generated by the system 200 according to an example. FIG. 7shows a predicted channel image as determined by the machine learningrandom forest model that may be trained with the data. Again, a firstsection 702 represents a channel and a second section 704 represents anabsence of the channel. FIG. 7 indicates a size and location of thechannel with respect to a depth level index and an azimuth in degrees.Thus, the random forest regression algorithm may be trained with data(e.g., integral amplitudes) that may be affected by various factors anda corresponding channel specification (e.g., location and size).Alternatively, a different attribute other than integral amplitudes maybe used. The trained model may be used to accurately predict channelsfrom data in the field that may be affected by factors such as mudconditions and receiver behavior.

Having disclosed some example system components and concepts, thedisclosure now turns to FIG. 8, which illustrates an example method 800for detecting a channel behind casing and generating an image thatrepresents the channel. For the sake of clarity, the method 800 isdescribed in terms of the channel detection system 200, as shown in FIG.2, configured to practice the method. The steps outlined herein areexemplary and can be implemented in any combination thereof, includingcombinations that exclude, add, or modify certain steps.

At step 802, the channel detection system 200 can determine integralamplitude for one or more azimuthal receivers to generate training data.The training data may be synthetic data and/or data that representsknown channels behind casing. At step 804, the channel detection system200 can train one or more random forest models using the training data.At step 806, the channel detection system 200 can test predictionaccuracy of the random forest models. At step 808, the channel detectionsystem 200 can use the random forest models to predict channels fromintegral amplitudes measured from sonic waveform data at depths ofinterest. At step 810, the channel detection system 200 can generate achannel image and/or a channel map that represents the absence and/orpresence of a channel behind casing based on the waveform data. Thechannel image may be a two-dimensional image or another type of image.

FIG. 9 illustrates another method 900 for determining an integralamplitude attribute. For the sake of clarity, the method 900 isdescribed in terms of the channel detection system 200, as shown in FIG.2, configured to practice the method. The steps outlined herein areexemplary and can be implemented in any combination thereof, includingcombinations that exclude, add, or modify certain steps.

At step 902, the channel detection system 200 can de-trend and/orresample waveform data from one or more receivers of the tool. Thewaveform data may be sampled at too high of a frequency and some of thedata may be discarded or not considered. At step 904, the channeldetection system 200 can determine peak based envelopes of the rectifiedwaveform data. At step 906, the channel detection system 200 canmodulate the sinusoid signal with an envelope. At step 908, the channeldetection system 200 can extract a first period of modulated signal andintegrate to obtain an integral amplitude attribute.

FIG. 10 illustrates a method 1000 for detecting a channel behind casingand generating an image that represents the channel. For the sake ofclarity, the method 1000 is described in terms of the channel detectionsystem 200, as shown in FIG. 2, configured to practice the method. Thesteps outlined herein are exemplary and can be implemented in anycombination thereof, including combinations that exclude, add, or modifycertain steps.

At step 1002, the channel detection system 200 can receive data samplesassociated with at least one casing, each data sample representingchannel information behind a representative casing. At step 1004, thechannel detection system 200 can train a machine learning model usingthe data samples to generate a mapping between waveform information ineach of the data samples and the channel information behind therepresentative casing. At step 1006, the channel detection system 200can receive acoustic data from the tool 212. The acoustic data mayrepresent a particular casing. At step 1008, the channel detectionsystem 200 can use the machine learning model to analyze the acousticdata from the tool 212 and determine of a presence and an absence of achannel behind the particular casing at a plurality of depths. At step1010, the channel detection system 200 can generate an azimuthal cementbond depth channel image that represents the presence and the absence ofthe channel behind the particular casing at the plurality of depths.

The channel detection system 200 can generate an azimuthal cement bonddepth channel image that represents the presence and the absence of thechannel behind the particular casing at the plurality of depths. Thechannel image may be a binary two-dimensional image. In addition, thebinary two-dimensional image can represent a size and an azimuthallocation of the channel behind the particular casing at the plurality ofdepths. The location may be an azimuthal location. The machine learningmodel can be based on random forest, among other algorithms.

Additionally, the tool 212 can be at least one array of receiversazimuthally arranged along a circumference of the tool. The array ofreceivers can determine an integral amplitude for waveform data obtainedby the at least one array of receivers. Additionally, a ring ofreceivers can be one of three feet and five feet from the casing.

Having disclosed example systems, methods, and technologies fordetecting a channel behind casing and generating an image thatrepresents the channel, the disclosure now turns to FIG. 11, whichillustrates an example computing device architecture 1100 which can beemployed to perform various steps, methods, and techniques disclosedherein. The various implementations will be apparent to those ofordinary skill in the art when practicing the present technology.Persons of ordinary skill in the art will also readily appreciate thatother system implementations or examples are possible.

As noted above, FIG. 11 illustrates an example computing devicearchitecture 1100 of a computing device which can implement the varioustechnologies and techniques described herein. For example, the computingdevice architecture 1100 can implement the system 200 shown in FIG. 2and perform various steps, methods, and techniques disclosed herein. Thecomponents of the computing device architecture 1100 are shown inelectrical communication with each other using a connection 1105, suchas a bus. The example computing device architecture 1100 includes aprocessing unit (CPU or processor) 1110 and a computing deviceconnection 1105 that couples various computing device componentsincluding the computing device memory 1115, such as read only memory(ROM) 1120 and random access memory (RAM) 1125, to the processor 1110.

The computing device architecture 1100 can include a cache of high-speedmemory connected directly with, in close proximity to, or integrated aspart of the processor 1110. The computing device architecture 1100 cancopy data from the memory 1115 and/or the storage device 1130 to thecache 1112 for quick access by the processor 1110. In this way, thecache can provide a performance boost that avoids processor 1110 delayswhile waiting for data. These and other modules can control or beconfigured to control the processor 1110 to perform various actions.Other computing device memory 1115 may be available for use as well. Thememory 1115 can include multiple different types of memory withdifferent performance characteristics. The processor 1110 can includeany general purpose processor and a hardware or software service, suchas service 1 1132, service 2 1134, and service 3 1136 stored in storagedevice 1130, configured to control the processor 1110 as well as aspecial-purpose processor where software instructions are incorporatedinto the processor design. The processor 1110 may be a self-containedsystem, containing multiple cores or processors, a bus, memorycontroller, cache, etc. A multi-core processor may be symmetric orasymmetric.

To enable user interaction with the computing device architecture 1100,an input device 1145 can represent any number of input mechanisms, suchas a microphone for speech, a touch-sensitive screen for gesture orgraphical input, keyboard, mouse, motion input, speech and so forth. Anoutput device 1135 can also be one or more of a number of outputmechanisms known to those of skill in the art, such as a display,projector, television, speaker device, etc. In some instances,multimodal computing devices can enable a user to provide multiple typesof input to communicate with the computing device architecture 1100. Thecommunications interface 1140 can generally govern and manage the userinput and computing device output. There is no restriction on operatingon any particular hardware arrangement and therefore the basic featureshere may easily be substituted for improved hardware or firmwarearrangements as they are developed.

Storage device 1130 is a non-volatile memory and can be a hard disk orother types of computer readable media which can store data that areaccessible by a computer, such as magnetic cassettes, flash memorycards, solid state memory devices, digital versatile disks, cartridges,random access memories (RAMs) 1125, read only memory (ROM) 1120, andhybrids thereof. The storage device 1130 can include services 1132,1134, 1136 for controlling the processor 1110. Other hardware orsoftware modules are contemplated. The storage device 1130 can beconnected to the computing device connection 1105. In one aspect, ahardware module that performs a particular function can include thesoftware component stored in a computer-readable medium in connectionwith the necessary hardware components, such as the processor 1110,connection 1105, output device 1135, and so forth, to carry out thefunction.

For clarity of explanation, in some instances the present technology maybe presented as including individual functional blocks includingfunctional blocks comprising devices, device components, steps orroutines in a method embodied in software, or combinations of hardwareand software.

In some embodiments the computer-readable storage devices, mediums, andmemories can include a cable or wireless signal containing a bit streamand the like. However, when mentioned, non-transitory computer-readablestorage media expressly exclude media such as energy, carrier signals,electromagnetic waves, and signals per se.

Methods according to the above-described examples can be implementedusing computer-executable instructions that are stored or otherwiseavailable from computer readable media. Such instructions can include,for example, instructions and data, which cause or otherwise configure ageneral purpose computer, special purpose computer, or a processingdevice to perform a certain function or group of functions. Portions ofcomputer resources used can be accessible over a network. The computerexecutable instructions may be, for example, binaries, intermediateformat instructions such as assembly language, firmware, source code,etc. Examples of computer-readable media that may be used to storeinstructions, information used, and/or information created duringmethods according to described examples include magnetic or opticaldisks, flash memory, USB devices provided with non-volatile memory,networked storage devices, and so on.

Devices implementing methods according to these disclosures can includehardware, firmware and/or software, and can take any of a variety ofform factors. Typical examples of such form factors include laptops,smart phones, small form factor personal computers, personal digitalassistants, rackmount devices, standalone devices, and so on.Functionality described herein also can be embodied in peripherals oradd-in cards. Such functionality can also be implemented on a circuitboard among different chips or different processes executing in a singledevice, by way of further example.

The instructions, media for conveying such instructions, computingresources for executing them, and other structures for supporting suchcomputing resources are example means for providing the functionsdescribed in the disclosure.

In the foregoing description, aspects of the application are describedwith reference to specific embodiments thereof, but those skilled in theart will recognize that the application is not limited thereto. Thus,while illustrative embodiments of the application have been described indetail herein, it is to be understood that the disclosed concepts may beotherwise variously embodied and employed, and that the appended claimsare intended to be construed to include such variations, except aslimited by the prior art. Various features and aspects of theabove-described subject matter may be used individually or jointly.Further, embodiments can be utilized in any number of environments andapplications beyond those described herein without departing from thebroader spirit and scope of the specification. The specification anddrawings are, accordingly, to be regarded as illustrative rather thanrestrictive. For the purposes of illustration, methods were described ina particular order. It should be appreciated that in alternateembodiments, the methods may be performed in a different order than thatdescribed.

Where components are described as being “configured to” perform certainoperations, such configuration can be accomplished, for example, bydesigning electronic circuits or other hardware to perform theoperation, by programming programmable electronic circuits (e.g.,microprocessors, or other suitable electronic circuits) to perform theoperation, or any combination thereof.

The various illustrative logical blocks, modules, circuits, andalgorithm steps described in connection with the examples disclosedherein may be implemented as electronic hardware, computer software,firmware, or combinations thereof. To clearly illustrate thisinterchangeability of hardware and software, various illustrativecomponents, blocks, modules, circuits, and steps have been describedabove generally in terms of their functionality. Whether suchfunctionality is implemented as hardware or software depends upon theparticular application and design constraints imposed on the overallsystem. Skilled artisans may implement the described functionality invarying ways for each particular application, but such implementationdecisions should not be interpreted as causing a departure from thescope of the present application.

The techniques described herein may also be implemented in electronichardware, computer software, firmware, or any combination thereof. Suchtechniques may be implemented in any of a variety of devices such asgeneral purpose computers, wireless communication device handsets, orintegrated circuit devices having multiple uses including application inwireless communication device handsets and other devices. Any featuresdescribed as modules or components may be implemented together in anintegrated logic device or separately as discrete but interoperablelogic devices. If implemented in software, the techniques may berealized at least in part by a computer-readable data storage mediumcomprising program code including instructions that, when executed,performs one or more of the method, algorithms, and/or operationsdescribed above. The computer-readable data storage medium may form partof a computer program product, which may include packaging materials.

The computer-readable medium may include memory or data storage media,such as random access memory (RAM) such as synchronous dynamic randomaccess memory (SDRAM), read-only memory (ROM), non-volatile randomaccess memory (NVRAM), electrically erasable programmable read-onlymemory (EEPROM), FLASH memory, magnetic or optical data storage media,and the like. The techniques additionally, or alternatively, may berealized at least in part by a computer-readable communication mediumthat carries or communicates program code in the form of instructions ordata structures and that can be accessed, read, and/or executed by acomputer, such as propagated signals or waves.

Other embodiments of the disclosure may be practiced in networkcomputing environments with many types of computer systemconfigurations, including personal computers, hand-held devices,multi-processor systems, microprocessor-based or programmable consumerelectronics, network PCs, minicomputers, mainframe computers, and thelike. Embodiments may also be practiced in distributed computingenvironments where tasks are performed by local and remote processingdevices that are linked (either by hardwired links, wireless links, orby a combination thereof) through a communications network. In adistributed computing environment, program modules may be located inboth local and remote memory storage devices.

It will be appreciated that for simplicity and clarity of illustration,where appropriate, reference numerals have been repeated among thedifferent figures to indicate corresponding or analogous elements. Inaddition, numerous specific details are set forth in order to provide athorough understanding of the embodiments described herein. However, itwill be understood by those of ordinary skill in the art that theembodiments described herein can be practiced without these specificdetails. In other instances, methods, procedures and components have notbeen described in detail so as not to obscure the related relevantfeature being described. Also, the description is not to be consideredas limiting the scope of the embodiments described herein. The drawingsare not necessarily to scale and the proportions of certain parts havebeen exaggerated to better illustrate details and features of thepresent disclosure.

In the above description, terms such as “upper,” “upward,” “lower,”“downward,” “above,” “below,” “downhole,” “uphole,” “longitudinal,”“lateral,” and the like, as used herein, shall mean in relation to thebottom or furthest extent of the surrounding wellbore even though thewellbore or portions of it may be deviated or horizontal.Correspondingly, the transverse, axial, lateral, longitudinal, radial,etc., orientations shall mean orientations relative to the orientationof the wellbore or tool. Additionally, the illustrate embodiments areillustrated such that the orientation is such that the right-hand sideis downhole compared to the left-hand side.

The term “coupled” is defined as connected, whether directly orindirectly through intervening components, and is not necessarilylimited to physical connections. The connection can be such that theobjects are permanently connected or releasably connected. The term“outside” refers to a region that is beyond the outermost confines of aphysical object. The term “inside” indicate that at least a portion of aregion is partially contained within a boundary formed by the object.The term “substantially” is defined to be essentially conforming to theparticular dimension, shape or other word that substantially modifies,such that the component need not be exact. For example, substantiallycylindrical means that the object resembles a cylinder, but can have oneor more deviations from a true cylinder.

The term “radially” means substantially in a direction along a radius ofthe object, or having a directional component in a direction along aradius of the object, even if the object is not exactly circular orcylindrical. The term “axially” means substantially along a direction ofthe axis of the object. If not specified, the term axially is such thatit refers to the longer axis of the object.

Although a variety of information was used to explain aspects within thescope of the appended claims, no limitation of the claims should beimplied based on particular features or arrangements, as one of ordinaryskill would be able to derive a wide variety of implementations. Furtherand although some subject matter may have been described in languagespecific to structural features and/or method steps, it is to beunderstood that the subject matter defined in the appended claims is notnecessarily limited to these described features or acts. Suchfunctionality can be distributed differently or performed in componentsother than those identified herein. The described features and steps aredisclosed as possible components of systems and methods within the scopeof the appended claims.

Moreover, claim language reciting “at least one of” a set indicates thatone member of the set or multiple members of the set satisfy the claim.For example, claim language reciting “at least one of A and B” means A,B, or A and B.

Statements of the disclosure include:

Statement 1: A method comprising receiving, by at least one processor,data samples associated with at least one casing, each data samplerepresenting channel information behind a representative casing,training, by the at least one processor, a machine learning model usingthe data samples to generate a mapping between waveform information ineach of the data samples and the channel information behind therepresentative casing, receiving, by the at least one processor,acoustic data from a tool, the acoustic data representing a particularcasing, and using, by the at least one processor, the machine learningmodel to analyze the acoustic data from the tool and determine one of apresence and an absence of a channel behind the particular casing at aplurality of depths.

Statement 2: A method according to Statement 1, further comprisinggenerating an azimuthal cement bond depth channel image that representsthe presence and the absence of the channel behind the particular casingat the plurality of depths.

Statement 3: A method according to any of Statements 1 and 2, whereinthe channel image is a binary two-dimensional image.

Statement 4: A method according to any of Statements 1 through 3,wherein the binary two-dimensional image represents a size and anazimuthal location of the channel behind the particular casing at theplurality of depths.

Statement 5: A method according to any of Statements 1 through 4,wherein the tool comprises at least one array of receivers azimuthallyarranged along a circumference of the tool.

Statement 6: A method according to any of Statements 1 through 5,further comprising determining an integral amplitude for waveform dataobtained by the at least one array of receivers.

Statement 7: A method according to any of Statements 1 through 6,wherein a ring of receivers is one of three feet and five feet from thecasing.

Statement 8: A method according to any of Statements 1 through 7,wherein the machine learning model is based on random forest.

Statement 9: A system comprising an acoustic tool comprising at leastone sensor, at least one processor, and at least one computer-readablestorage medium having stored therein instructions, which when executedby the at least one processor cause the system to: receive data samplesassociated with at least one casing, each data sample representingchannel information behind a representative casing, train a machinelearning model using the data samples to generate a mapping betweenwaveform information in each of the data samples and the channelinformation behind the representative casing, receive acoustic data fromthe tool, the acoustic data representing a particular casing, and usethe machine learning model to analyze the acoustic data from the tooland determine one of a presence and an absence of a channel behind theparticular casing at a plurality of depths.

Statement 10: A system according to Statement 9, the at least oneprocessor further to generate an azimuthal cement bond depth channelimage that represents the presence and the absence of the channel behindthe particular casing at the plurality of depths.

Statement 11: A system according to any of Statements 9 and 10, whereinthe channel image is a binary two-dimensional image.

Statement 12: A system according to any of Statements 9 through 11,wherein the binary two-dimensional image represents a size and anazimuthal location of the channel behind the particular casing at theplurality of depths.

Statement 13: A system according to any of Statements 9 through 12,wherein the acoustic tool comprises at least one array of receiversazimuthally arranged along a circumference of the tool.

Statement 14: A system according to any of Statements 9 through 13, theat least one processor further to dynamically sense in real-time, by theat least one sensor, the first indication to transition from thepowered-off state to the low power standby state.

Statement 14: A system according to any of Statements 8 through 13, theat least one processor further to determine an integral amplitude forwaveform data obtained by the at least one array of receivers.

Statement 15: A system according to any of Statements 9 through 14,wherein a ring of receivers is one of three feet and five feet from thecasing.

Statement 16: A system according to any of Statements 9 through 15,wherein the machine learning model is based on random forest.

Statement 17: A non-transitory computer-readable storage mediumcomprising instructions stored on the non-transitory computer-readablestorage medium, the instructions, when executed by one more processors,cause the one or more processors to perform operations including:receiving data samples associated with at least one casing, each datasample representing channel information behind a representative casing,training a machine learning model using the data samples to generate amapping between waveform information in each of the data samples and thechannel information behind the representative casing, receiving acousticdata from a tool, the acoustic data representing a particular casing,and using the machine learning model to analyze the acoustic data fromthe tool and determine one of a presence and an absence of a channelbehind the particular casing at a plurality of depths.

Statement 18: A non-transitory computer-readable storage mediumaccording to Statement 17, the operations further comprising generatingan azimuthal cement bond depth channel image that represents thepresence and the absence of the channel behind the particular casing atthe plurality of depths.

Statement 19: A non-transitory computer-readable storage mediumaccording to any of Statements 17 and 18, wherein the channel image is abinary two-dimensional image.

Statement 20: A non-transitory computer-readable storage mediumaccording to any of Statements 17 through 19, wherein the binarytwo-dimensional image represents a size and an azimuthal location of thechannel behind the particular casing at the plurality of depths.

Statement 21: A system comprising means for performing a methodaccording to any of Statements 1 through 8.

1. A method comprising: receiving, by at least one processor, datasamples associated with at least one casing, each data samplerepresenting channel information behind a representative casing;training, by the at least one processor, a machine learning model usingthe data samples to generate a mapping between integral amplitudescalculated from waveform information in each of the data samples and thechannel information behind the representative casing, wherein at leastone integral amplitude is calculated by rectifying the waveforminformation, determining a peaks-based envelope of the rectifiedwaveform information, using the peaks-based envelope to modulate asinusoid signal, and integrating a period of the sinusoid signal toobtain the integral amplitude; receiving, by the at least one processor,acoustic data from a tool, the acoustic data representing a particularcasing; calculating a plurality of integral amplitudes based on theacoustic data from the tool; and using, by the at least one processor,the machine learning model to analyze the calculated plurality ofintegral amplitudes from the acoustic data and determine one of apresence and an absence of a channel behind the particular casing at aplurality of depths.
 2. The method of claim 1, further comprisinggenerating an azimuthal cement bond depth channel image that representsthe presence and the absence of the channel behind the particular casingat the plurality of depths.
 3. The method of claim 2, wherein thechannel image is a binary two-dimensional image.
 4. The method of claim3, wherein the binary two-dimensional image represents a size and anazimuthal location of the channel behind the particular casing at theplurality of depths.
 5. The method of claim 1, wherein the toolcomprises at least one array of receivers azimuthally arranged along acircumference of the tool.
 6. The method of claim 5, wherein the toolcomprises a monopole transmitter that transmits waves having a frequencyless than ultrasound frequencies.
 7. The method of claim 5, wherein thetool comprises a monopole transmitter, and wherein a ring of receiversis one of three feet and five feet from the monopole transmitter.
 8. Themethod of claim 1, wherein the machine learning model is a regressionmodel based on random forest.
 9. A system comprising: an acoustic toolcomprising at least one sensor; at least one processor; and at least onecomputer-readable storage medium having stored therein instructions,which when executed by the at least one processor cause the system to:receive data samples associated with at least one casing, each datasample representing channel information behind a representative casing;train a machine learning model using the data samples to generate amapping between integral amplitudes calculated from waveform informationin each of the data samples and the channel information behind therepresentative casing, wherein at least one integral amplitude iscalculated by rectifying the waveform information, determining apeaks-based envelope of the rectified waveform information, using thepeaks-based envelope to modulate a sinusoid signal, and integrating aperiod of the sinusoid signal to obtain the integral amplitude; receiveacoustic data from the tool, the acoustic data representing a particularcasing; calculate a plurality of integral amplitudes based on theacoustic data from the tool; and use the machine learning model toanalyze the calculated plurality of integral amplitudes from theacoustic data from the tool and determine one of a presence and anabsence of a channel behind the particular casing at a plurality ofdepths.
 10. The system of claim 9, the at least one processor further togenerate an azimuthal cement bond depth channel image that representsthe presence and the absence of the channel behind the particular casingat the plurality of depths.
 11. The system of claim 10, wherein thechannel image is a binary two-dimensional image.
 12. The system of claim11, wherein the binary two-dimensional image represents a size and anazimuthal location of the channel behind the particular casing at theplurality of depths.
 13. The system of claim 9, wherein the acoustictool comprises at least one array of receivers azimuthally arrangedalong a circumference of the tool.
 14. The system of claim 13, whereinthe acoustic tool comprises a monopole transmitter that transmits waveshaving a frequency less than ultrasound frequencies.
 15. The system ofclaim 13, wherein the acoustic tool comprises a monopole transmitter,and wherein a ring of receivers is one of three feet and five feet fromthe monopole transmitter.
 16. The system of claim 9, wherein the machinelearning model is a regression model based on random forest.
 17. Anon-transitory computer-readable medium having instructions storedthereon that, when executed by at least one processor, cause the atleast one processor to perform operations comprising: receiving datasamples associated with at least one casing, each data samplerepresenting channel information behind a representative casing;training a machine learning model using the data samples to generate amapping between integral amplitudes calculated from waveform informationin each of the data samples and the channel information behind therepresentative casing, wherein at least one integral amplitude iscalculated by rectifying the waveform information, determining apeaks-based envelope of the rectified waveform information, using thepeaks-based envelope to modulate a sinusoid signal, and integrating aperiod of the sinusoid signal to obtain the integral amplitude;receiving acoustic data from a tool, the acoustic data representing aparticular casing; calculating a plurality of integral amplitudes basedon the acoustic data from the tool; and using the machine learning modelto analyze the calculated plurality of integral amplitudes from theacoustic data and determine one of a presence and an absence of achannel behind the particular casing at a plurality of depths.
 18. Thenon-transitory computer-readable medium of claim 17, the operationsfurther comprising generating an azimuthal cement bond depth channelimage that represents the presence and the absence of the channel behindthe particular casing at the plurality of depths.
 19. The non-transitorycomputer-readable medium of claim 18, wherein the channel image is abinary two-dimensional image.
 20. The non-transitory computer-readablemedium of claim 19, wherein the binary two-dimensional image representsa size and an azimuthal location of the channel behind the particularcasing at the plurality of depths.